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Horticulturae
Special Issues
Remote and Proximal Sensing Technologies Applied to Precision Agriculture
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Remote and Proximal Sensing Technologies Applied to Precision Agriculture

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Protected Culture".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 10433

Special Issue Editor

Dr. Stefano Marino

E-Mail Website
Guest Editor
Department of Agricultural, Environmental and Food Sciences (DAEFS), University of Molise, I-86100 Campobasso, Italy
Interests: agronomy; precision agriculture; crop yield; fertilization; irrigation; remote sensing; horticultural crops; vegetation indices
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

New remote and proximal sensing techniques and approaches should be able to meet future agriculture and food demands while reducing the environmental impact of agriculture. The use of remote sensing technologies for precision agriculture has increased rapidly during the past few decades. The development and steep rise of remote and proximal sensing technologies has marked a new era in remote sensing, providing data of unprecedented spatial, spectral, and temporal resolution.

Data derived by imaging sensors, the availability of complementary data (e.g., weather forecasting, soil information derived by sensor networks), emerging technologies, such as geospatial technologies, the Internet of Things (IoT), and artificial intelligence (AI) could be utilized to make informed management decisions aiming to increase crop production, quality, and traceability.

This Special Issue is aimed at a global research community involved in data analysis, sensor and tool development, and data acquisition for precision agriculture in open fields and greenhouses. As such, it is open to anyone conducting research in the field of precision agriculture.

Prof. Dr. Stefano Marino
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Horticulturae is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing from satellites or unmanned aerial vehicles
  • proximal sensors
  • artificial intelligence, big data analysis, and IoT tools
  • in situ remote sensing measurements (e.g., robotic vision, sensors)
  • automated machinery and agricultural tools and robots
  • multi-/hyperspectral, fluorescence, thermal, LiDAR, and SAR
  • soil, nutrient, water, weeds, disease, and pest management
  • yield prediction and quality
  • traceability, phenotyping estimation, crop monitoring, and mapping
  • greenhouses and open field crops

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Published Papers (2 papers)

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18 pages, 2103 KiB  
Article
Ripeness Prediction in Table Grape Cultivars by Using a Portable NIR Device
by Giuseppe Ferrara, Valerio Marcotuli, Angelo Didonna, Anna Maria Stellacci, Marino Palasciano and Andrea Mazzeo
Horticulturae 2022, 8(7), 613; https://doi.org/10.3390/horticulturae8070613 - 7 Jul 2022
Cited by 17 | Viewed by 3943
Abstract
In the past years, near infrared (NIR) spectroscopy has been applied to the agricultural industry as a non-destructive tool to predict quality parameters, e.g., ripeness of fruit, dry matter content, and acidity. In two years, 2019 and 2020, berries of four table grape [...] Read more.
In the past years, near infrared (NIR) spectroscopy has been applied to the agricultural industry as a non-destructive tool to predict quality parameters, e.g., ripeness of fruit, dry matter content, and acidity. In two years, 2019 and 2020, berries of four table grape cultivars (Cotton Candy™, Summer Royal, Allison™, and Autumncrisp®) were collected during the season to obtain spectral measurements and quality data for developing predictive models based on NIR spectroscopy to be practically used in the vineyard. A SCiO™ sensor was used in 2019 for predicting the ripening parameters of Cotton Candy™; in particular, total soluble solids (TSS) (R2 = 0.95; RMSE = 0.60, RPD = 13.13), titratable acidity (R2 = 0.97; RMSE = 0.40, RPD = 7.31), and pH (R2 = 0.96; RMSE = 0.07, RPD = 26.06). With these promising results, in the year 2020, the above-mentioned table grape cultivars were all tested for TSS prediction with successful outcomes: Cotton Candy™ (R2 = 0.97; RMSE = 0.68, RPD = 7.48), Summer Royal (R2 = 0.96; RMSE = 0.83, RPD = 7.13), Allison™ (R2 = 0.97; RMSE = 0.72, RPD = 8.70) and Autumncrisp® (R2 = 0.96; RMSE = 0.60, RPD = 9.73). In conclusion, a rapid and economic sensor such as the SCiO™ device can enable a practical application in the vineyard to assess ripening (quality) parameters of table grapes. Thus, this device or similar ones can be also used for a fast sorting and screening of quality throughout the supply chain, from vineyard to cold storage. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Technologies Applied to Precision Agriculture)
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14 pages, 3940 KiB  
Article
Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm
by Jizhang Wang, Zhiheng Gao, Yun Zhang, Jing Zhou, Jianzhi Wu and Pingping Li
Horticulturae 2022, 8(1), 21; https://doi.org/10.3390/horticulturae8010021 - 24 Dec 2021
Cited by 29 | Viewed by 5802
Abstract
In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an [...] Read more.
In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing Technologies Applied to Precision Agriculture)
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